2019 Neukom Research Prize Winners

Neukom Prize for Outstanding Graduate Research

The Wigner Current for Open Quantum Systems

William F. Braash, Jr., Oscar D. Friedman, Alexander J. Rimberg, and Miles P. Blencowe

We  extend the  Wigner  current  vector  field (Wigner current) construct to  single  bosonic  mode quantum systems  interacting with  an  environment.  In  terms  of the  Wigner  function quasiproba- bility  density and  associated Wigner  current, the  open system  quantum dynamics can be concisely expressed  as a continuity equation. Through the  consideration of the  harmonic oscillator  and  addi- tively  driven  Duffing oscillator  in the  bistable regime  as illustrative system  examples, we show how the  evolving  Wigner  current vector  field on the  system  phase  space yields useful geometric insights concerning how  quantum states decohere  away  due  to  interactions with  the  environment, as well as how they  may  be stabilized through the  counteracting effects of the  system  anharmonicity (i.e., nonlinearity).

Neukom Prize for Outstanding Undergraduate Research

Modeling Semantic Encoding in a Common Neural Representational Space

Cara E. Van Uden, Samuel A. Nastase, Andrew C. Connelly, Ma Feilong, Isabella Hansen, M. Ida Gobbini, and James V. Haxby

Encoding models for mapping voxelwise semantic tuning are typically estimated separately for each individual, limiting their generalizability. In the current report, we develop a method for estimating semantic encoding models that generalize across individuals. Functional MRI was used to measure brain responses while participants freely viewed a naturalistic audiovisual movie. Word embeddings capturing agent-, action-, object-, and scene-related semantic content were assigned to each imaging volume based on an annotation of the film. We constructed both conventional withinsubject semantic encoding models and between-subject models where the model was trained on a subset of participants and validated on a left-out participant. Between subject models were trained using cortical surface-based anatomical normalization or surface-based whole-cortex hyperalignment. We used hyperalignment to project group data into an individual’s unique anatomical space via a common representational space, thus leveraging a larger volume of data for out-of-sample prediction while preserving the individual’s fine-grained functional–anatomical idiosyncrasies. Our findings demonstrate that anatomical normalization degrades the spatial specificity of between-subject encoding models relative to within-subject models. Hyperalignment, on the other hand, recovers the spatial specificity of semantic tuning lost during anatomical normalization, and yields model performance exceeding that of within-subject models.